An ordering of pixels where the most important pixels are ranked highest will cause a steep decrease in the output confidence of the model. This can be summarized by computing the area under the perturbation curve, where a low AUC corresponds to a good explanation.
[1]} also introduces an alternative variant of the Deletion metric, where the pixels are masked in reversed order of importance. In that case, a high AUC value indicates a good attribution map. We call this variant Deletion\(_{LeRF}\)  (Least Relevant First), and the original Deletion\(_{MoRF}\)  (Most Relevant First).
\(Del_{MoRF}(\mathbf {x}, m, \mathbf {e}, c) &= \frac{1}{L} \sum _{k=1}^L m(\mathbf {x}^\mathbf {e}_{k})_c \\Del_{LeRF}(\mathbf {x}, m, \mathbf {e}, c) &= \frac{1}{L} \sum _{k=1}^L m(\mathbf {x}^\mathbf {e}_{-k})_c\) 
